用SEAIHRDS疾病传播模型模拟和预测COVID-19在塞尔维亚共和国的传播

IF 3 4区 环境科学与生态学 Q2 ENVIRONMENTAL SCIENCES
Slavoljub Stanojevic , Mirza Ponjavic , Slobodan Stanojevic , Aleksandar Stevanovic , Sonja Radojicic
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引用次数: 3

摘要

为应对由SARS-Cov-2病毒引起的大流行,塞尔维亚共和国于2020年3月15日采取了全面的抗流行病措施,以遏制COVID-19。疫情趋缓后,监管部门于2020年5月6日决定放宽实施措施。然而,流行病学形势很快再次恶化。截至2021年2月7日,塞尔维亚共报告了406352例sars -2感染病例,其中4112例死于COVID-19。为了更好地了解疫情动态并预测可能的结果,我们建立了自适应数学模型SEAIHRDS (s -易感、e -暴露、a -无症状、i -感染、h -住院、r -康复、d-因COVID-19感染死亡、s -易感)。该模型可用于模拟实施干预措施的各种情景,并计算可能的流行病结果,包括必要的医院能力。考虑到针对COVID-19疫苗开发的有希望的结果,该模型被扩展到模拟不同人群阶层的疫苗接种。各种模拟情景的结果表明,通过实施严格的减少接触措施,可以控制COVID-19并减少死亡人数。调查结果还表明,在最易受感染的人口阶层中限制有效接触值得特别注意。然而,研究结果也表明,这种疾病有可能在人群中长期存在,可能具有季节性模式。如果研制出效力等于或高于65%的疫苗,将有助于显著减缓或完全阻止病毒在人群中的传播。疫苗接种的效果主要取决于:1。现有疫苗的效力,2。2 .确定接种人群类别的优先次序;人口的总体疫苗接种覆盖率,假设疫苗在接种者中产生坚实的免疫力。预期基本繁殖数Ro=2.46,疫苗效力为68%,87%的覆盖率足以阻止病毒传播。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Simulation and prediction of spread of COVID-19 in The Republic of Serbia by SEAIHRDS model of disease transmission

Simulation and prediction of spread of COVID-19 in The Republic of Serbia by SEAIHRDS model of disease transmission

As a response to the pandemic caused by SARS-Cov-2 virus, on 15 March 2020, the Republic of Serbia introduced comprehensive anti-epidemic measures to curb COVID-19. After a slowdown in the epidemic, on 6 May 2020, the regulatory authorities decided to relax the implemented measures. However, the epidemiological situation soon worsened again. As of 7 February 2021, a total of 406,352 cases of SARSCov-2 infection have been reported in Serbia, 4,112 deaths caused by COVID-19. In order to better understand the epidemic dynamics and predict possible outcomes, we have developed an adaptive mathematical model SEAIHRDS (S-susceptible, E-exposed, A-asymptomatic, I-infected, H-hospitalized, R-recovered, d-dead due to COVID-19 infection, S-susceptible). The model can be used to simulate various scenarios of the implemented intervention measures and calculate possible epidemic outcomes, including the necessary hospital capacities. Considering promising results regarding the development of a vaccine against COVID-19, the model is extended to simulate vaccination among different population strata. The findings from various simulation scenarios have shown that, with implementation of strict measures of contact reduction, it is possible to control COVID-19 and reduce number of deaths. The findings also show that limiting effective contacts within the most susceptible population strata merits a special attention. However, the findings also show that the disease has a potential to remain in the population for a long time, likely with a seasonal pattern. If a vaccine, with efficacy equal or higher than 65%, becomes available it could help to significantly slow down or completely stop circulation of the virus in human population.

The effects of vaccination depend primarily on: 1. Efficacy of available vaccine(s), 2. Prioritization of the population categories for vaccination, and 3. Overall vaccination coverage of the population, assuming that the vaccine(s) develop solid immunity in vaccinated individuals. With expected basic reproduction number of Ro=2.46 and vaccine efficacy of 68%, an 87% coverage would be sufficient to stop the virus circulation.

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来源期刊
Microbial Risk Analysis
Microbial Risk Analysis Medicine-Microbiology (medical)
CiteScore
5.70
自引率
7.10%
发文量
28
审稿时长
52 days
期刊介绍: The journal Microbial Risk Analysis accepts articles dealing with the study of risk analysis applied to microbial hazards. Manuscripts should at least cover any of the components of risk assessment (risk characterization, exposure assessment, etc.), risk management and/or risk communication in any microbiology field (clinical, environmental, food, veterinary, etc.). This journal also accepts article dealing with predictive microbiology, quantitative microbial ecology, mathematical modeling, risk studies applied to microbial ecology, quantitative microbiology for epidemiological studies, statistical methods applied to microbiology, and laws and regulatory policies aimed at lessening the risk of microbial hazards. Work focusing on risk studies of viruses, parasites, microbial toxins, antimicrobial resistant organisms, genetically modified organisms (GMOs), and recombinant DNA products are also acceptable.
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